What is Cortex.dev?
It is an open source platform that takes machine learning models—trained with nearly any framework—and turns them into production web APIs in one command.
Cortex.dev is a tool in the Machine Learning Tools category of a tech stack.
Cortex.dev is an open source tool with 7.9K GitHub stars and 606 GitHub forks. Here’s a link to Cortex.dev's open source repository on GitHub
Who uses Cortex.dev?
Developers
7 developers on StackShare have stated that they use Cortex.dev.
Cortex.dev Integrations
TensorFlow, PyTorch, scikit-learn, Keras, and XGBoost are some of the popular tools that integrate with Cortex.dev. Here's a list of all 5 tools that integrate with Cortex.dev.
Cortex.dev's Features
- Autoscaling
- Supports TensorFlow, Keras, PyTorch, Scikit-learn, XGBoost, and more
- CPU / GPU support
- Rolling updates
- Log streaming
- Prediction monitoring
- Minimal declarative configuration
Cortex.dev Alternatives & Comparisons
What are some alternatives to Cortex.dev?
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
CUDA
A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
